data lake

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Published By: SnowFlake     Published Date: Jul 08, 2016
This EMA case study profiles the implementation of the Snowflake Elastic Data Warehouse, a new generation of cloud-based data warehouses, by Accordant Media. This document details significant tangible and intangible improvements and opportunities the Snowflake solution created for the Accordant Media infrastructure and analytical teams.
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snowflake, media, data, technology, cloud-based data, best practices
    
SnowFlake
Published By: SnowFlake     Published Date: Jul 08, 2016
In the era of big data, enterprise data warehouse (EDW) technology continues to evolve as vendors focus on innovation and advanced features around in-memory, compression, security, and tighter integration with Hadoop, NoSQL, and cloud. Forrester identified the 10 most significant EDW software and services providers — Actian, Amazon Web Services (AWS), Hewlett Packard Enterprise (HPE), IBM, Microsoft, Oracle, Pivotal Software, SAP, Snowflake Computing, and Teradata — in the category and researched, analyzed, and scored them. This report details our findings about how well each vendor fulfills our criteria and where they stand in relation to each other to help enterprise architect professionals select the right solution to support their data warehouse platform.
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forrester, enterprise, data, technology, best practices, innovation, security
    
SnowFlake
Published By: SAS     Published Date: Mar 06, 2018
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics, and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. With the right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
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SAS
Published By: IBM     Published Date: Nov 30, 2017
Analyst firm, Enterprise Strategy Group, examines how companies can leverage cloud-based data lakes and self-service analytics for timely business insights that weren’t possible until now. And learn how IBM Cloud Object Storage, as a persistent storage layer, powers analytics and business intelligence solutions on the IBM Cloud. Complete the form to download the analyst paper.
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analytics, technology, digital transformation, data lake, always-on data lake, ibm, cloud-based analytics
    
IBM
Published By: SAS     Published Date: Aug 28, 2018
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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SAS
Published By: SAS     Published Date: Apr 25, 2017
Organizations in pursuit of data-driven goals are seeking to extend and expand business intelligence (BI) and analytics to more users and functions. Users want to tap new data sources, including Hadoop files. However, organizations are feeling pain because as the data becomes more challenging, data preparation processes are getting longer, more complex, and more inefficient. They also demand too much IT involvement. New technology solutions and practices are providing alternatives that increase self-service data preparation, address inefficiencies, and make it easier to work with Hadoop data lakes. This report will examine organizations’ challenges with data preparation and discuss technologies and best practices for making improvements.
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SAS
Published By: SAS     Published Date: Oct 18, 2017
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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SAS
Published By: EMC     Published Date: Jun 13, 2016
A Data Lake can meet the storage needs of your Modern Data Center. Check out the Top 10 Reasons your organization should adopt scale-out data lake storage for Hadoop Analytics on EMC Isilon.
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emc, data center, storage, storage space, data protection
    
EMC
Published By: EMC     Published Date: Jun 13, 2016
EMC Isilon Cloudpools software provides policy-based automated tiering that lets you seamlessly integrate with the cloud as on additional storage tier for the isilon cluster at your data center.
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emc, cloud computing, data center management, clusters, data integration
    
EMC
Published By: NetApp     Published Date: Jun 27, 2016
"Gartner: Moving Toward the All Solid-State Storage Data Center Are you only using solid-state arrays for your primary data? If so, you’re missing out on the benefits flash can deliver to other applications, such as active archives, data lakes, and big data infrastructures. In this independent report, Gartner finds that progressive I&O leaders are already moving toward an all solid-state data center and predicts that others will soon follow. Read the report here."
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NetApp
Published By: NetApp     Published Date: Aug 26, 2016
Gartner: Moving Toward the All Solid-State Storage Data Center Are you only using solid-state arrays for your primary data? If so, you’re missing out on the benefits flash can deliver to other applications, such as active archives, data lakes, and big data infrastructures. In this independent report, Gartner finds that progressive I&O leaders are already moving toward an all solid-state data center and predicts that others will soon follow. Read the report here.
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netapp, database performance, flash storage, data management, cost challenges
    
NetApp
Published By: Amazon Web Services     Published Date: Jul 25, 2018
Organisationen müssen heute mit immer größeren Datenmengen zurechtkommen, die aus mehr Datenquellen stammen und mehr Datentypen enthalten als jemals zuvor. Angesichts massiver, heterogener Datenmengen stellen viele Organisationen fest, dass sie eine Datenspeicher- und Analyselösung benötigen, die höhere Geschwindigkeit und mehr Flexibilität als ältere Systeme bietet, um rechtzeitig geschäftliche Erkenntnisse liefern zu können. Ein Data Lake ist eine neue und zunehmend populäre Möglichkeit zur Speicherung und Analyse von Daten, die viele dieser Herausforderungen meistert, indem sie es einer Organisation ermöglicht, alle Daten in einem zentralen Repository zu speichern. Da Daten in ihrem ursprünglichen Format gespeichert werden können, besteht kein Bedarf, sie vor der Übernahme in ein vordefiniertes Schema zu konvertieren, wodurch Sie die Möglichkeit erhalten, all Ihre Daten, sowohl strukturiert als auch unstrukturiert, mit minimaler Vorlaufzeit zu speichern.
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Amazon Web Services
Published By: Attunity     Published Date: Nov 15, 2018
IT departments today face serious data integration hurdles when adopting and managing a Hadoop-based data lake. Many lack the ETL and Hadoop coding skills required to replicate data across these large environments. In this whitepaper, learn how you can provide automated Data Lake pipelines that accelerate and streamline your data lake ingestion efforts, enabling IT to deliver more data, ready for agile analytics, to the business.
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Attunity
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